REVIEW 1 cited by
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance. To support users in determining well-performing hyperparameter configurations for their algorithms, datasets and applications at hand, SMAC3 offers a robust and flexible framework for Bayesian Optimization, which can improve performance within a few evaluations. It offers several facades and pre-sets for typical use cases, such as optimizing hyperparameters, solving low dimensional continuous (artificial) global optimization problems and configuring algorithms to perform well across multiple problem instances. The SMAC3 package is available under a permissive BSD-license at https://github.com/automl/SMAC3.
Forward citations
Cited by 1 Pith paper
-
RAG-Stack: Co-Optimizing RAG Serving Performance and Quality
RAG-Stack jointly optimizes RAG algorithm choices and serving-system settings via sub-metric-aware multi-objective Bayesian optimization plus an analytical performance model, reporting Pareto frontiers covering 52.5% ...
Discussion (0). Sign in to comment.